Single-cell RNA-seq data analysis

# R Options
options(stringsAsFactors=FALSE)

# Required libraries
library(dplyr)
library(Seurat)
library(ggplot2)
library(openxlsx)
library(reticulate)
library(MAST)
library(biomaRt)
library(enrichR)
library(ggpubr)
library(knitr)
library(kableExtra)
library(dplyr)
library(tidyr)
library(ggsci)

use_python('/usr/bin/python')

# ggplot style
plot.mystyle = function(p, title=NULL, col=NULL, legend.title=NULL, legend.position=NULL) {
  p = p + theme_light() + theme(panel.border = element_blank())
  if(!is.null(title)) p = p + ggtitle(title) + theme(plot.title = element_text(hjust=0.5))
  if(length(col) > 0) p = p + scale_fill_manual(values=col)
  if(!is.null(legend.title)) {
    p = p + labs(color=legend.title, fill=legend.title)
  } else {
    p = p + theme(legend.title = element_blank()) 
  }
  if(!is.null(legend.position)) p = p + theme(legend.position=legend.position)
  return(p)
}

Current project

• 10X published dataset
  
• Single Cell Gene Expression Dataset by Cell Ranger 3.0.0
  
• 1k Peripheral blood mononuclear cells (PBMCs) from a healthy donor (v3 chemistry)

Project-specific parameters

Open this code chunk to read all parameters that are set specifically for your project.

param = list()

# Project ID
param$project = "pbmc"

# Project-specific paths
param$path = "~/scrnaseq_refDatasets/10x_pbmc_1k_healthyDonor_v3Chemistry/"
param$path.out = paste0(param$path, "/Seurat/")
if(!file.exists(param$path.out)) dir.create(param$path.out)

# Input data path in case Cell Ranger was run 
param$path.data = "~/scrnaseq_refDatasets/10x_pbmc_1k_healthyDonor_v3Chemistry/filtered_feature_bc_matrix/"

param$file.mapping.stats = "~/scrnaseq_refDatasets/10x_pbmc_1k_healthyDonor_v3Chemistry/pbmc_1k_v3_metrics_summary.csv"
param$file.annot = "~/ensembl/hsapiens_gene_ensembl.98.csv"

# In case of HTO, readable names for Hashtag Oligos 
param$hto.names = NULL

# Prefix of mitochondrial genes 
param$mt = "^MT-"

# Biomart dataset to use for gene name translations
param$mart.dataset = "hsapiens_gene_ensembl"

# The number of PCs to use; adjust this parameter based on JackStraw and Elbowplot 
param$pc.n = 7

# Resolution of clusters; low values will lead to fewer clusters of cells 
param$cluster.resolution=0.5

# Thresholds to define differentially expressed genes 
param$padj = 0.05
param$log2fc = log2(1.5)

# Marker genes based on literature 
# https://icb-scanpy-tutorials.readthedocs-hosted.com/en/latest/visualizing-marker-genes.html
param$known.markers = list()
param$known.markers[["bcell"]] = c("CD79A", "MS4A1")
param$known.markers[["tcell"]] = "CD3D"
param$known.markers[["tcell.cd8+"]] = c("CD8A", "CD8B")
param$known.markers[["nk"]] = c("GNLY", "NKG7")
param$known.markers[["myeloid"]] = c("CST3", "LYZ")
param$known.markers[["monocytes"]] = "FCGR3A"
param$known.markers[["dendritic"]] = "FCER1A"

# Enrichr databases of interest
param$enrichr.dbs = c("GO_Molecular_Function_2018", "GO_Biological_Process_2018", "GO_Cellular_Component_2018")

# Main color to use for plots
param$col = "palevioletred"

Read data

Read and print mapping statistics

We begin by printing mapping statistics that have been produced prior to this workflow.

if(!is.null(param$file.mapping.stats)){
  mapping.stats = as.data.frame(t(read.delim(param$file.mapping.stats, sep=",", header=TRUE, check.names=FALSE)))
  colnames(mapping.stats) = "Value"
  kable(mapping.stats, align="l", caption="Mapping statistics") %>% 
    kable_styling(bootstrap_options = c("striped", "hover"), full_width=FALSE, position="left")
} else { 
  message("Mapping statistics cannot be shown. No valid file provided.")
}

Mapping statistics

	Value
Estimated Number of Cells	1,222
Mean Reads per Cell	54,502
Median Genes per Cell	1,919
Number of Reads	66,601,887
Valid Barcodes	97.4%
Sequencing Saturation	70.8%
Q30 Bases in Barcode	94.1%
Q30 Bases in RNA Read	90.2%
Q30 Bases in Sample Index	91.1%
Q30 Bases in UMI	92.7%
Reads Mapped to Genome	95.4%
Reads Mapped Confidently to Genome	92.4%
Reads Mapped Confidently to Intergenic Regions	4.8%
Reads Mapped Confidently to Intronic Regions	31.1%
Reads Mapped Confidently to Exonic Regions	56.5%
Reads Mapped Confidently to Transcriptome	53.7%
Reads Mapped Antisense to Gene	1.0%
Fraction Reads in Cells	94.9%
Total Genes Detected	18,391
Median UMI Counts per Cell	6,628

Setup the Seurat object

We next read the scRNA-seq counts table to initialise a Seurat object.

# Load the dataset
sc.data = Read10X(param$path.data)

# Initialize the Seurat object with the raw (non-normalized data).
sc = CreateSeuratObject(counts=sc.data, project=param$project, min.cells=3)
sc

## An object of class Seurat 
## 15247 features across 1222 samples within 1 assay 
## Active assay: RNA (15247 features)

Read gene annotation

We finally read additional gene annotation from Ensembl, and translate Ensembl IDs to Entrez gene symbols. The resulting table is written to file.

# Read feature IDs: Ensembl ID, 10X gene symbol 
gene.ids = read.delim(paste0(param$path.data, "features.tsv.gz"), header=FALSE)
gene.ids = gene.ids[gene.ids[,3]=="Gene Expression", 1:2]
colnames(gene.ids) = c("Ensembl", "GeneSymbol")

# Seurat does this as well, so we need to replicate it
gene.ids[,"GeneSymbol"] = make.unique(gene.ids[,"GeneSymbol"]) 

# When you call "CreateSeuratObject", underscores "_" in gene names are replaced with minus "-"
gene.ids = cbind(gene.ids, GeneSymbolEdited=gsub(gene.ids[,"GeneSymbol"], pattern="_", replacement="-", fixed=TRUE))

# Retrieve Entrez Gene Symbols; this is needed for EnrichR functional enrichment analysis
#mart = useMart("ensembl", dataset=param$mart.dataset)
#mapping = getBM(
#  filters="ensembl_gene_id",
#  attributes=c("ensembl_gene_id", "entrezgene_accession"),
#  values=gene.ids[,"Ensembl"],
#  mart=mart)

# Note that we get the first Entrez Symbol that matches the Ensembl ID
#mapping.match = match(gene.ids[,"Ensembl"], mapping[,"ensembl_gene_id"])
#gene.ids = cbind(gene.ids, EntrezSymbol=mapping[mapping.match,"entrezgene_accession"])
gene.ids = cbind(gene.ids, EntrezSymbol=gene.ids[,"GeneSymbol"])

# Write table
write.table(gene.ids, file=paste0(param$path.out, "/GeneIds.txt"), sep="\t", row.names=TRUE, col.names=TRUE, quote=FALSE)

# Create translation table
symbol.to.ensembl = setNames(gene.ids[,"Ensembl"], gene.ids[,"GeneSymbolEdited"])
symbol.to.entrez = setNames(gene.ids[,"EntrezSymbol"], gene.ids[,"GeneSymbolEdited"])

# Read Ensembl annotation
annot.ensembl = read.delim(param$file.annot, row.names=1)

Pre-processing

Quality control

We start the analysis by removing unwanted cells from the dataset. Three commonly used QC metrics include the number of unique genes detected in each cell (“nFeature”), the total number of molecules detected in each cell (“nCount”), and the percentage of reads that map to the mitochrondrial genome (“percent.mt”).

# Calculate percentage of reads that map to mitochondrial genome 
sc <- PercentageFeatureSet(sc, pattern=param$mt, col.name="percent.mt")

# Metadata 
kable(head(sc@meta.data, 5), align="l", caption="Meta-data, top 5 rows") %>% 
  kable_styling(bootstrap_options = c("striped", "hover"), full_width=FALSE, position="left")

Meta-data, top 5 rows

	orig.ident	nCount_RNA	nFeature_RNA	percent.mt
AAACCCAAGGAGAGTA	pbmc	8286	2618	10.777215
AAACGCTTCAGCCCAG	pbmc	5509	1805	7.968778
AAAGAACAGACGACTG	pbmc	4280	1559	6.191589
AAAGAACCAATGGCAG	pbmc	2754	1225	5.991285
AAAGAACGTCTGCAAT	pbmc	6589	1828	6.617089

# Filter cutoffs
features.cut = c(nFeature_RNA=200, nCount_RNA=NA, percent.mt=20)

# Plot QC metrics
features = c("nFeature_RNA", "nCount_RNA", "percent.mt")
p = VlnPlot(sc, features=features, pt.size=0, col=param$col, combine=FALSE) 
names(p) = features
for(i in features) {
  p[[i]] = plot.mystyle(p[[i]], title=i, legend.position="none") + xlab("")
  if(!is.na(features.cut[i])) p[[i]] = p[[i]] + geom_hline(yintercept=features.cut[i], lty=2, col="darkgrey") 
}
p = CombinePlots(p, ncol=3)
p = plot.mystyle(p, title="Distribution of feature values")
p 

# Correlate QC metrics
p = list()
p[[1]] = FeatureScatter(sc, feature1=features[2], feature2=features[1], cols=param$col)
p[[2]] = FeatureScatter(sc, feature1=features[2], feature2=features[3], cols=param$col)
for(i in 1:length(p)) p[[i]] = plot.mystyle(p[[i]], legend.position="none")
p = CombinePlots(p)
p = plot.mystyle(p, title="Features plotted against each other")
p

# Actual filtering
sc = subset(sc, subset=nFeature_RNA>features.cut["nFeature_RNA"] & percent.mt<features.cut["percent.mt"])
sc

## An object of class Seurat 
## 15247 features across 1113 samples within 1 assay 
## Active assay: RNA (15247 features)

Normalisation, feature selection and scaling

Feature selection: For downstream analysis it is beneficial to focus on genes that exhibit high cell-to-cell variation, that is they are highly expressed in some cells and lowly expressed in others.

Scaling: To be able to compare normalised gene counts between genes, gene counts are further scaled to have zero mean and unit variance (z-score). This way, genes are equally weighted for downstream analysis.

# Normalise data the original way
sc = NormalizeData(sc, normalization.method = "LogNormalize", scale.factor = 10000)

# Select features from normalised data
sc = FindVariableFeatures(sc, selection.method = "vst", nfeatures = 2000)

# Scale normalised data
all.genes = rownames(sc)
sc = ScaleData(sc, features=all.genes)

# Run sctransform
# This is a new normalisation method that replaces previous Seurat functions 'NormalizeData', 'FindVariableFeatures', and 'ScaleData'. 
# vignette: https://satijalab.org/seurat/v3.0/sctransform_vignette.html
# paper: https://www.biorxiv.org/content/10.1101/576827v2
# normalised data end up here: sc@assays$SCT@data
sc = SCTransform(sc)

# Show variable genes
top10 = head(VariableFeatures(sc), 10)

# Plot variable features with and without labels
p1 = VariableFeaturePlot(sc, cols=c("grey", param$col)) 
p1 = plot.mystyle(p1)
p2 = LabelPoints(plot=p1, points=top10, repel=TRUE, cols=c("grey", param$col))
p = CombinePlots(plots=list(p1, p2), legend="bottom")
p = plot.mystyle(p, title="Variable genes without (left) and with (right) labels")
p

Dimensionality reduction

A single-cell dataset of 20,000 genes and 5,000 cells has 20,000 dimensions. The biological manifold however can be described by far fewer dimensions than the number of genes. Dimension reduction methods aim to find these dimensions. There are two general purposes for dimension reduction methods: to summarise a dataset, and to visualise a dataset.

We use Principal Component Analysis (PCA) to summarise a dataset, overcoming noise and reducing the data to its essential components. Each principal component (PC) represents a “metafeature” that combines information across a correlated gene set. Later, we use Uniform Manifold Approximation and Projection (UMAP) to visualise the dataset, placing similar cells together in 2D space, see below.

To decide how many PCs to include in downstream analyses, we visualize cells and genes that define the PCA.

sc = RunPCA(sc, features=VariableFeatures(object=sc))

p = VizDimLoadings(sc, dims=1:2, reduction="pca", col=param$col, combine=FALSE)
for(i in 1:length(p)) p[[i]] = plot.mystyle(p[[i]])
p = CombinePlots(p)
p = plot.mystyle(p, title="Top gene loadings of the first two PCs")
p

if(run.hto) { 
  p = DimPlot(sc, reduction="pca", cols=param$col.hto.collapsed)
} else {
  p = DimPlot(sc, reduction="pca")
}
p = plot.mystyle(p, title="Cells arranged by the first two PCs", legend.title="HTO")
p

DimHeatmap(sc, dims=1, cells=500, balanced=TRUE)

DimHeatmap(sc, dims=1:20, cells=500, balanced=TRUE)

Dimensionality of the dataset

We next need to decide how many PCs we want to use for downstream analyses. The following two plots are designed to help us make an informed decision.

The first plot is based on the “JackStraw” procedure: parts of the data is repeatedly randomly permuted and PCA is rerun, generating a “null distribution” of feature scores. Significant PCs are those with a strong enrichment of low p-value features.

The second plot is an “Elbow plot”: PCs are ranked based on the percentage of variance they explain.

For your dataset, we decided to go for 7 PCs.

# NOTE: This process can take a long time for big datasets, comment out for expediency. More
# approximate techniques such as those implemented in ElbowPlot() can be used to reduce
# computation time
sc = JackStraw(sc, num.replicate=100, dims=20)
sc = ScoreJackStraw(sc, dims=1:20)
p = JackStrawPlot(sc, dims=1:20)
p = plot.mystyle(p, title="Jack Straw plot", legend.position="bottom")
p

p = ElbowPlot(sc, ndims=20)
p = plot.mystyle(p, title="Elbow plot")
p

Downstream analysis

Clustering

Seurat’s clustering method first constructs a graph structure, where nodes are cells and edges are drawn between cells with similar gene expression patterns. Technically speaking, Seurat first constructs a K-nearest neighbor (KNN) graph based on Euclidean distance in PCA space, and refines edge weights between cells based on the shared overlap in their local neighborhoods (Jaccard similarity). To partition the graph into highly interconnected parts, cells are iteratively grouped together using the Leiden algorithm.

# Note: I changed the seed in ./lib/python3.6/site-packages/leidenalg/functions.py to 11 for reproducibility
# The number of clusters can be optimized by tuning 'resolution' -> based on feedback from the client whether or not clusters make sense

# Choose the number of PCs to use for clustering
sc = FindNeighbors(sc, dims=1:param$pc.n)

# Cluster using the Leiden algorithm
# Paper to Leiden algorithm: https://www.nature.com/articles/s41598-019-41695-z
# Seurat vignette suggests resolution parameter between 0.4-1.2 for datasets of about 3k cells
sc = FindClusters(sc, resolution=param$cluster.resolution, algorithm=4)

Visualisation with UMAP

We use a UMAP to visualise and explore a dataset. The goal is to place similar cells together in 2D space, and learn about the biology underlying the data. Cells are color-coded according to the graph-based clustering, and clusters typcially co-localise on the UMAP.

Take care not to mis-read a UMAP:

• Parameters influence the plot (we use defaults here)
  
• Cluster sizes relative to each other mean nothing, since the method has a local notion of distance
  
• Distances between clusters might not mean anything
  
• You may need more than one plot

For a nice read to intuitively understand UMAP, see https://pair-code.github.io/understanding-umap/.

sc = RunUMAP(sc, dims=1:param$pc.n)

## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session

# Note that you can set `label = TRUE` or use the LabelClusters function to help label individual clusters
cluster.cells = table(sc@active.ident)
cluster.labels = paste0(levels(sc@active.ident)," (", cluster.cells[levels(sc@active.ident)],")")
p = DimPlot(sc, reduction="umap", label=TRUE) + scale_colour_discrete("Cluster", labels=cluster.labels)
p = plot.mystyle(p, "UMAP, cells coloured by cluster identity", legend.position="bottom")
p

Feature plots QC

Do cells in individual clusters have particularly high counts, detected genes or mitochondrial content?

p = FeaturePlot(sc, features=features, cols=c("lightgrey", param$col), combine=FALSE)
names(p) = features
for(i in features) p[[i]] = plot.mystyle(p[[i]], title=i)
CombinePlots(p, ncol=2)

Feature plots for known marker genes

Do cells in individual clusters express provided known marker genes?

g = unique(unlist(param$known.markers))
g = g[length(g):1]
d = DotPlot(sc, features=g, cols=c("lightgrey", param$col))
d + theme(axis.text.x = element_text(angle=90, hjust=1, vjust=.5))

for(i in 1:length(param$known.markers)) {
  f = FeaturePlot(sc, features=param$known.markers[[i]], cols=c("lightgrey", param$col))
  f = ggpubr::annotate_figure(p=f, top = ggpubr::text_grob(label=names(param$known.markers)[i], face='bold'))
  print(f)
}

Differentially expressed genes

We next identify genes that are differentially expressed in one cluster compared to all other clusters. Additional gene annotation is added, and the resulting tables are written to file.

# Find markers for every cluster compared to all remaining cells, report only the positive ones
# min.pct = requires feature to be detected at this minimum percentage in either of the two groups of cells 
# logfc.threshold = requires a feature to be differentially expressed on average by some amount between the two groups
# only.pos = find only positive markers 

# Review recommends using "MAST"; Mathias uses "LR"
# ALWAYS USE: assay="RNA" or assay="SCT"
# DONT USE: assay=integrated datasets; this data is normalised and contains only 2k genes
sc.markers = FindAllMarkers(sc, assay="RNA", only.pos=FALSE, min.pct=0.25, logfc.threshold=0.25, test.use="MAST")
sc.markers.top2 = sc.markers %>% group_by(cluster) %>% top_n(n=2, wt=avg_logFC) %>% as.data.frame
kable(sc.markers.top2, align="l", caption="Top 2 DEGs per cell cluster") %>% 
  kable_styling(bootstrap_options = c("striped", "hover"), full_width=FALSE, position="left")

Top 2 DEGs per cell cluster

p_val	avg_logFC	pct.1	pct.2	p_val_adj	cluster	gene
0	3.8292753	1.000	0.156	0	1	S100A8
0	3.4714042	1.000	0.204	0	1	S100A9
0	1.3654774	0.938	0.255	0	2	IL7R
0	1.0531971	0.964	0.341	0	2	TRAC
0	3.6609018	0.871	0.025	0	3	IGHM
0	4.9139410	0.796	0.237	0	3	IGKC
0	0.9279829	0.759	0.154	0	4	CCR7
0	0.9427734	0.927	0.499	0	4	NOSIP
0	2.0182653	0.837	0.049	0	5	GZMK
0	1.8890308	0.748	0.140	0	5	KLRB1
0	1.7451108	1.000	0.293	0	6	CST3
0	1.6522173	1.000	0.514	0	6	HLA-DPA1
0	3.8593508	0.980	0.073	0	7	GNLY
0	2.9277180	1.000	0.187	0	7	NKG7
0	5.0496622	1.000	0.237	0	8	NRGN
0	5.9002407	1.000	0.042	0	8	PPBP

# Add Ensembl annotation
sc.markers.ensembl = symbol.to.ensembl[sc.markers[,"gene"]]
sc.markers.annot = cbind(sc.markers, annot.ensembl[sc.markers.ensembl,])

# Output in Excel sheet
sc.markers.lst = lapply(levels(sc.markers.annot$cluster), function(x){sc.markers.annot %>% dplyr::filter(cluster==x)})
names(sc.markers.lst) = paste0("cluster", levels(sc.markers$cluster))
write.xlsx(sc.markers.lst, file=paste0(param$path.out, "Markers.xlsx"))

# Filter markers based on p-value and fold-change 
sc.markers.filt = sc.markers %>% dplyr::filter(p_val_adj <= param$padj) %>% dplyr::filter((avg_logFC <= -param$log2fc) | (avg_logFC >= param$log2fc)) %>% as.data.frame
sc.markers.filt.down = sc.markers.filt %>% dplyr::filter(avg_logFC <= -param$log2fc) %>% as.data.frame
sc.markers.filt.up = sc.markers.filt %>% dplyr::filter(avg_logFC >= param$log2fc) %>% as.data.frame

# Number of DEGs per cluster
cluster.all = sort(unique(sc.markers[,"cluster"]))
sc.markers.filt.n = cbind(Cluster=cluster.all, 
                          sc.markers.filt.down %>% dplyr::count(cluster) %>% transmute(Down=n),
                          sc.markers.filt.up %>% dplyr::count(cluster) %>% transmute(Up=n)) %>% 
  pivot_longer(cols=c("Down", "Up"), names_to="Direction", values_to="n")

p = ggplot(sc.markers.filt.n, aes(x=Cluster, y=n, fill=Direction)) + geom_bar(stat="identity") 
p = plot.mystyle(p, title=paste0("Number of DEGs per cell cluster\n(FC=", 2^param$log2fc, ", adj. p-value=", param$padj, ")"), col=c("steelblue", "darkgoldenrod1"))
p

Visualisation of differentially expressed genes

The following plots are exemplary to how we can visualize differentially expressed genes using the “Seurat” R-package. The selected genes are the top differentially expressed genes for cluster 1 to 3, respectively.

# Get top gene per cluster and plot
genes.example = sc.markers %>% group_by(cluster) %>% top_n(n=1, wt=avg_logFC) %>% pull(gene)
genes.example = genes.example[1:3]

# Shows gene expression on the UMAP
p = FeaturePlot(sc, features=genes.example, cols=c("lightgrey", param$col), combine=FALSE)
names(p) = genes.example
for(i in names(p)) p[[i]] = plot.mystyle(p[[i]], title=i)
p = CombinePlots(p)
p = p + ggtitle("UMAP, cells coloured by normalised gene expression data") + theme(plot.title = element_text(hjust=0.5))
p

# Ridge plot of normalised gene expression data
p = RidgePlot(sc, features=genes.example, combine=FALSE)
names(p) = genes.example
for(i in names(p)) p[[i]] = plot.mystyle(p[[i]], title=i, legend.title="Cell classification")
p = CombinePlots(p, legend="bottom", ncol=2)
p = plot.mystyle(p, "Ridge plot of normalised gene expression data") 
p

# Ridge plot of raw gene expression counts
p = RidgePlot(sc, features=genes.example[1:3], slot="counts", combine=FALSE)
names(p) = genes.example
for(i in names(p)) p[[i]] = plot.mystyle(p[[i]], title=i, legend.title="Cell classification")
p = CombinePlots(p, legend="bottom", ncol=2)
p = plot.mystyle(p, title="Ridge plot of raw gene expression counts")
p

# Visualises how feature expression changes across different clusters
p = DotPlot(sc, features=genes.example[3:1], cols=c("lightgrey", param$col))
p = plot.mystyle(p, title="Dot plot of normalised gene expression data")
p

# Heatmap of top differentially expressed genes
top = sc.markers %>% group_by(cluster) %>% top_n(n=10, wt=avg_logFC)
DoHeatmap(sc, features=top$gene) + NoLegend()

## Warning in DoHeatmap(sc, features = top$gene): The following features were
## omitted as they were not found in the scale.data slot for the SCT assay: NPM1

Functional enrichment analysis

To gain first insights into potential functions of cells in a cluster, we test for over-representation of functional terms amongst up- and down-regulated genes of each cluster. Over-represented terms are written to file.

We first translate gene symbols of up- and down-regulated genes per cluster into Entrez gene symbols, and then use the “enrichR” R-package to access the “Enrichr” website https://amp.pharm.mssm.edu/Enrichr/. You can choose to test functional enrichment from a wide range of databases:

dbs.all = listEnrichrDbs()
kable(dbs.all, align="l", caption="Enrichr databases") %>% 
  kable_styling(bootstrap_options = c("striped", "hover"), full_width=FALSE, position="left") %>% 
  scroll_box(width = "100%", height = "300px")

Enrichr databases

geneCoverage	genesPerTerm	libraryName	link	numTerms
13362	275	Genome_Browser_PWMs	http://hgdownload.cse.ucsc.edu/goldenPath/hg18/database/	615
27884	1284	TRANSFAC_and_JASPAR_PWMs	http://jaspar.genereg.net/html/DOWNLOAD/	326
6002	77	Transcription_Factor_PPIs		290
47172	1370	ChEA_2013	http://amp.pharm.mssm.edu/lib/cheadownload.jsp	353
47107	509	Drug_Perturbations_from_GEO_2014	http://www.ncbi.nlm.nih.gov/geo/	701
21493	3713	ENCODE_TF_ChIP-seq_2014	http://genome.ucsc.edu/ENCODE/downloads.html	498
1295	18	BioCarta_2013	https://cgap.nci.nih.gov/Pathways/BioCarta_Pathways	249
3185	73	Reactome_2013	http://www.reactome.org/download/index.html	78
2854	34	WikiPathways_2013	http://www.wikipathways.org/index.php/Download_Pathways	199
15057	300	Disease_Signatures_from_GEO_up_2014	http://www.ncbi.nlm.nih.gov/geo/	142
4128	48	KEGG_2013	http://www.kegg.jp/kegg/download/	200
34061	641	TF-LOF_Expression_from_GEO	http://www.ncbi.nlm.nih.gov/geo/	269
7504	155	TargetScan_microRNA	http://www.targetscan.org/cgi-bin/targetscan/data_download.cgi?db=vert_61	222
16399	247	PPI_Hub_Proteins	http://amp.pharm.mssm.edu/X2K	385
12753	57	GO_Molecular_Function_2015	http://www.geneontology.org/GO.downloads.annotations.shtml	1136
23726	127	GeneSigDB	http://genesigdb.org/genesigdb/downloadall.jsp	2139
32740	85	Chromosome_Location	http://software.broadinstitute.org/gsea/msigdb/index.jsp	386
13373	258	Human_Gene_Atlas	http://biogps.org/downloads/	84
19270	388	Mouse_Gene_Atlas	http://biogps.org/downloads/	96
13236	82	GO_Cellular_Component_2015	http://www.geneontology.org/GO.downloads.annotations.shtml	641
14264	58	GO_Biological_Process_2015	http://www.geneontology.org/GO.downloads.annotations.shtml	5192
3096	31	Human_Phenotype_Ontology	http://www.human-phenotype-ontology.org/	1779
22288	4368	Epigenomics_Roadmap_HM_ChIP-seq	http://www.roadmapepigenomics.org/	383
4533	37	KEA_2013	http://amp.pharm.mssm.edu/lib/keacommandline.jsp	474
10231	158	NURSA_Human_Endogenous_Complexome	https://www.nursa.org/nursa/index.jsf	1796
2741	5	CORUM	http://mips.helmholtz-muenchen.de/genre/proj/corum/	1658
5655	342	SILAC_Phosphoproteomics	http://amp.pharm.mssm.edu/lib/keacommandline.jsp	84
10406	715	MGI_Mammalian_Phenotype_Level_3	http://www.informatics.jax.org/	71
10493	200	MGI_Mammalian_Phenotype_Level_4	http://www.informatics.jax.org/	476
11251	100	Old_CMAP_up	http://www.broadinstitute.org/cmap/	6100
8695	100	Old_CMAP_down	http://www.broadinstitute.org/cmap/	6100
1759	25	OMIM_Disease	http://www.omim.org/downloads	90
2178	89	OMIM_Expanded	http://www.omim.org/downloads	187
851	15	VirusMINT	http://mint.bio.uniroma2.it/download.html	85
10061	106	MSigDB_Computational	http://www.broadinstitute.org/gsea/msigdb/collections.jsp	858
11250	166	MSigDB_Oncogenic_Signatures	http://www.broadinstitute.org/gsea/msigdb/collections.jsp	189
15406	300	Disease_Signatures_from_GEO_down_2014	http://www.ncbi.nlm.nih.gov/geo/	142
17711	300	Virus_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	323
17576	300	Virus_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	323
15797	176	Cancer_Cell_Line_Encyclopedia	https://portals.broadinstitute.org/ccle/home	967
12232	343	NCI-60_Cancer_Cell_Lines	http://biogps.org/downloads/	93
13572	301	Tissue_Protein_Expression_from_ProteomicsDB	https://www.proteomicsdb.org/	207
6454	301	Tissue_Protein_Expression_from_Human_Proteome_Map	http://www.humanproteomemap.org/index.php	30
3723	47	HMDB_Metabolites	http://www.hmdb.ca/downloads	3906
7588	35	Pfam_InterPro_Domains	ftp://ftp.ebi.ac.uk/pub/databases/interpro/	311
7682	78	GO_Biological_Process_2013	http://www.geneontology.org/GO.downloads.annotations.shtml	941
7324	172	GO_Cellular_Component_2013	http://www.geneontology.org/GO.downloads.annotations.shtml	205
8469	122	GO_Molecular_Function_2013	http://www.geneontology.org/GO.downloads.annotations.shtml	402
13121	305	Allen_Brain_Atlas_up	http://www.brain-map.org/	2192
26382	1811	ENCODE_TF_ChIP-seq_2015	http://genome.ucsc.edu/ENCODE/downloads.html	816
29065	2123	ENCODE_Histone_Modifications_2015	http://genome.ucsc.edu/ENCODE/downloads.html	412
280	9	Phosphatase_Substrates_from_DEPOD	http://www.koehn.embl.de/depod/	59
13877	304	Allen_Brain_Atlas_down	http://www.brain-map.org/	2192
15852	912	ENCODE_Histone_Modifications_2013	http://genome.ucsc.edu/ENCODE/downloads.html	109
4320	129	Achilles_fitness_increase	http://www.broadinstitute.org/achilles	216
4271	128	Achilles_fitness_decrease	http://www.broadinstitute.org/achilles	216
10496	201	MGI_Mammalian_Phenotype_2013	http://www.informatics.jax.org/	476
1678	21	BioCarta_2015	https://cgap.nci.nih.gov/Pathways/BioCarta_Pathways	239
756	12	HumanCyc_2015	http://humancyc.org/	125
3800	48	KEGG_2015	http://www.kegg.jp/kegg/download/	179
2541	39	NCI-Nature_2015	http://pid.nci.nih.gov/	209
1918	39	Panther_2015	http://www.pantherdb.org/	104
5863	51	WikiPathways_2015	http://www.wikipathways.org/index.php/Download_Pathways	404
6768	47	Reactome_2015	http://www.reactome.org/download/index.html	1389
25651	807	ESCAPE	http://www.maayanlab.net/ESCAPE/	315
19129	1594	HomoloGene	http://www.ncbi.nlm.nih.gov/homologene	12
23939	293	Disease_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	839
23561	307	Disease_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	839
23877	302	Drug_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	906
15886	9	Genes_Associated_with_NIH_Grants	https://grants.nih.gov/grants/oer.htm	32876
24350	299	Drug_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	906
3102	25	KEA_2015	http://amp.pharm.mssm.edu/Enrichr	428
31132	298	Gene_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	2460
30832	302	Gene_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	2460
48230	1429	ChEA_2015	http://amp.pharm.mssm.edu/Enrichr	395
5613	36	dbGaP	http://www.ncbi.nlm.nih.gov/gap	345
9559	73	LINCS_L1000_Chem_Pert_up	https://clue.io/	33132
9448	63	LINCS_L1000_Chem_Pert_down	https://clue.io/	33132
16725	1443	GTEx_Tissue_Sample_Gene_Expression_Profiles_down	http://www.gtexportal.org/	2918
19249	1443	GTEx_Tissue_Sample_Gene_Expression_Profiles_up	http://www.gtexportal.org/	2918
15090	282	Ligand_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	261
16129	292	Aging_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	286
15309	308	Aging_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	286
15103	318	Ligand_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	261
15022	290	MCF7_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	401
15676	310	MCF7_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	401
15854	279	Microbe_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	312
15015	321	Microbe_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	312
3788	159	LINCS_L1000_Ligand_Perturbations_down	https://clue.io/	96
3357	153	LINCS_L1000_Ligand_Perturbations_up	https://clue.io/	96
12668	300	L1000_Kinase_and_GPCR_Perturbations_down	https://clue.io/	3644
12638	300	L1000_Kinase_and_GPCR_Perturbations_up	https://clue.io/	3644
8973	64	Reactome_2016	http://www.reactome.org/download/index.html	1530
7010	87	KEGG_2016	http://www.kegg.jp/kegg/download/	293
5966	51	WikiPathways_2016	http://www.wikipathways.org/index.php/Download_Pathways	437
15562	887	ENCODE_and_ChEA_Consensus_TFs_from_ChIP-X		104
17850	300	Kinase_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	285
17660	300	Kinase_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	285
1348	19	BioCarta_2016	http://cgap.nci.nih.gov/Pathways/BioCarta_Pathways	237
934	13	HumanCyc_2016	http://humancyc.org/	152
2541	39	NCI-Nature_2016	http://pid.nci.nih.gov/	209
2041	42	Panther_2016	http://www.pantherdb.org/pathway/	112
5209	300	DrugMatrix	https://ntp.niehs.nih.gov/drugmatrix/	7876
49238	1550	ChEA_2016	http://amp.pharm.mssm.edu/Enrichr	645
2243	19	huMAP	http://proteincomplexes.org/	995
19586	545	Jensen_TISSUES	http://tissues.jensenlab.org/	1842
22440	505	RNA-Seq_Disease_Gene_and_Drug_Signatures_from_GEO	http://www.ncbi.nlm.nih.gov/geo/	1302
8184	24	MGI_Mammalian_Phenotype_2017	http://www.informatics.jax.org/	5231
18329	161	Jensen_COMPARTMENTS	http://compartments.jensenlab.org/	2283
15755	28	Jensen_DISEASES	http://diseases.jensenlab.org/	1811
10271	22	BioPlex_2017	http://bioplex.hms.harvard.edu/	3915
10427	38	GO_Cellular_Component_2017	http://www.geneontology.org/	636
10601	25	GO_Molecular_Function_2017	http://www.geneontology.org/	972
13822	21	GO_Biological_Process_2017	http://www.geneontology.org/	3166
8002	143	GO_Cellular_Component_2017b	http://www.geneontology.org/	816
10089	45	GO_Molecular_Function_2017b	http://www.geneontology.org/	3271
13247	49	GO_Biological_Process_2017b	http://www.geneontology.org/	10125
21809	2316	ARCHS4_Tissues	http://amp.pharm.mssm.edu/archs4	108
23601	2395	ARCHS4_Cell-lines	http://amp.pharm.mssm.edu/archs4	125
20883	299	ARCHS4_IDG_Coexp	http://amp.pharm.mssm.edu/archs4	352
19612	299	ARCHS4_Kinases_Coexp	http://amp.pharm.mssm.edu/archs4	498
25983	299	ARCHS4_TFs_Coexp	http://amp.pharm.mssm.edu/archs4	1724
19500	137	SysMyo_Muscle_Gene_Sets	http://sys-myo.rhcloud.com/	1135
14893	128	miRTarBase_2017	http://mirtarbase.mbc.nctu.edu.tw/	3240
17598	1208	TargetScan_microRNA_2017	http://www.targetscan.org/	683
5902	109	Enrichr_Libraries_Most_Popular_Genes	http://amp.pharm.mssm.edu/Enrichr	121
12486	299	Enrichr_Submissions_TF-Gene_Coocurrence	http://amp.pharm.mssm.edu/Enrichr	1722
1073	100	Data_Acquisition_Method_Most_Popular_Genes	http://amp.pharm.mssm.edu/Enrichr	12
19513	117	DSigDB	http://tanlab.ucdenver.edu/DSigDB/DSigDBv1.0/	4026
14433	36	GO_Biological_Process_2018	http://www.geneontology.org/	5103
8655	61	GO_Cellular_Component_2018	http://www.geneontology.org/	446
11459	39	GO_Molecular_Function_2018	http://www.geneontology.org/	1151
19741	270	TF_Perturbations_Followed_by_Expression	http://www.ncbi.nlm.nih.gov/geo/	1958
27360	802	Chromosome_Location_hg19	http://hgdownload.cse.ucsc.edu/downloads.html	36
13072	26	NIH_Funded_PIs_2017_Human_GeneRIF	https://www.ncbi.nlm.nih.gov/pubmed/	5687
13464	45	NIH_Funded_PIs_2017_Human_AutoRIF	https://www.ncbi.nlm.nih.gov/pubmed/	12558
13787	200	Rare_Diseases_AutoRIF_ARCHS4_Predictions	https://amp.pharm.mssm.edu/geneshot/	3725
13929	200	Rare_Diseases_GeneRIF_ARCHS4_Predictions	https://www.ncbi.nlm.nih.gov/gene/about-generif	2244
16964	200	NIH_Funded_PIs_2017_AutoRIF_ARCHS4_Predictions	https://www.ncbi.nlm.nih.gov/pubmed/	12558
17258	200	NIH_Funded_PIs_2017_GeneRIF_ARCHS4_Predictions	https://www.ncbi.nlm.nih.gov/pubmed/	5684
10352	58	Rare_Diseases_GeneRIF_Gene_Lists	https://www.ncbi.nlm.nih.gov/gene/about-generif	2244
10471	76	Rare_Diseases_AutoRIF_Gene_Lists	https://amp.pharm.mssm.edu/geneshot/	3725
12419	491	SubCell_BarCode	http://www.subcellbarcode.org/	104
19378	37	GWAS_Catalog_2019	https://www.ebi.ac.uk/gwas	1737
6201	45	WikiPathways_2019_Human	https://www.wikipathways.org/	472
4558	54	WikiPathways_2019_Mouse	https://www.wikipathways.org/	176
3264	22	TRRUST_Transcription_Factors_2019	https://www.grnpedia.org/trrust/	571
7802	92	KEGG_2019_Human	https://www.kegg.jp/	308
8551	98	KEGG_2019_Mouse	https://www.kegg.jp/	303
12444	23	InterPro_Domains_2019	https://www.ebi.ac.uk/interpro/	1071
9000	20	Pfam_Domains_2019	https://pfam.xfam.org/	608
7744	363	DepMap_WG_CRISPR_Screens_Broad_CellLines_2019	https://depmap.org/	558
6204	387	DepMap_WG_CRISPR_Screens_Sanger_CellLines_2019	https://depmap.org/	325
13420	32	MGI_Mammalian_Phenotype_Level_4_2019	http://www.informatics.jax.org/	5261
14148	122	UK_Biobank_GWAS_v1	https://www.ukbiobank.ac.uk/tag/gwas/	857
9813	49	BioPlanet_2019	https://tripod.nih.gov/bioplanet/	1510
1397	13	ClinVar_2019	https://www.ncbi.nlm.nih.gov/clinvar/	182
9116	22	PheWeb_2019	http://pheweb.sph.umich.edu/	1161
17464	63	DisGeNET	https://www.disgenet.org	9828
394	73	HMS_LINCS_KinomeScan	http://lincs.hms.harvard.edu/kinomescan/	148
11851	586	CCLE_Proteomics_2020	https://portals.broadinstitute.org/ccle	378
8189	421	ProteomicsDB_2020	https://www.proteomicsdb.org/	913
18704	100	lncHUB_lncRNA_Co-Expression	https://amp.pharm.mssm.edu/lnchub/	3729
5605	39	Virus-Host_PPI_P-HIPSTer_2020	http://phipster.org/	6715
5718	31	Elsevier_Pathway_Collection	http://www.transgene.ru/disease-pathways/	1721

# DEGs up and down per cluster
cluster.all = sort(unique(sc.markers[,"cluster"]))
genesets.up = lapply(cluster.all, function(x) {
  tmp = sc.markers.filt.up %>% dplyr::filter(cluster==x) %>% dplyr::pull(gene)
  unique(na.exclude(symbol.to.entrez[tmp]))
})
genesets.down = lapply(cluster.all, function(x) {
  tmp = sc.markers.filt.down %>% dplyr::filter(cluster==x) %>% dplyr::pull(gene)
  unique(na.exclude(symbol.to.entrez[tmp]))
})
names(genesets.up) = paste0("DEG_up_cluster_", cluster.all)
names(genesets.down) = paste0("DEG_down_cluster_", cluster.all)
genesets = c(genesets.up, genesets.down)
  
# Loop through gene lists
enriched = list()
for(i in 1:length(genesets)) {
  if(length(genesets[[i]]) >= 3) {
    message("Geneset ", names(genesets)[i])
    enriched[[i]] = enrichr(genesets[[i]], databases=param$enrichr.dbs)
  } else { 
    message("Geneset ", names(genesets)[i], " has less than 3 genes; skip enrichr")
    enriched[[i]] = NULL
  }
}
names(enriched) = names(genesets)

# Write enrichment results to file
enriched.write = unlist(enriched, recursive=FALSE)
for(i in 1:length(enriched)) { 
  if(!is.null(enriched[[i]])) { 
    write.xlsx(enriched[[i]], file=paste0(param$path.out, "/Functions_", names(enriched)[i], ".xlsx"))
  }
}

Cell Cycle Effect

How much do gene expression profiles in your dataset reflect the cell cycle phases the single cells were in? We determine the effects of cell cycle heterogeneity by calculating a score for each cell based on its expression of G2M and S phase markers. Scoring is based on the strategy described in Tirosh et al. 2016, and human gene names are translated using biomaRt.

# Use biomart to translate human cell cycle genes to the species of interest
mart.human = useMart("ensembl", dataset="hsapiens_gene_ensembl")
mart.myspecies = useMart("ensembl", dataset=param$mart.dataset)
 
genes.s = getLDS(attributes=c("external_gene_name"), filters="external_gene_name", values=cc.genes.updated.2019$s.genes, mart=mart.human, attributesL=c("external_gene_name"), martL=mart.myspecies, uniqueRows=TRUE)

genes.g2m = getLDS(attributes=c("external_gene_name"), filters="external_gene_name", values=cc.genes.updated.2019$g2m.genes, mart=mart.human, attributesL=c("external_gene_name"), martL=mart.myspecies, uniqueRows=TRUE)

# Determine cell cycle effect
sc = CellCycleScoring(object=sc, s.features=genes.s[,2], g2m.features=genes.g2m[,2], set.ident=FALSE)

## Warning: The following features are not present in the object: EXO1, DTL, E2F8,
## CDC45, DSCC1, RAD51, not searching for symbol synonyms

## Warning: The following features are not present in the object: KIF2C, CDK1,
## PIMREG, DLGAP5, TTK, CDC25C, HMMR, NEK2, HJURP, GTSE1, KIF11, CDCA3, CKAP2L,
## CDCA2, CCNB2, UBE2C, CDC20, BUB1, CENPA, ANLN, not searching for symbol synonyms

# Get a feeling for how many cells are affected
p1 = ggplot(sc@meta.data, aes(x=S.Score, y=G2M.Score, colour=Phase)) + 
  geom_point() + 
  scale_x_continuous("G1/S score") + 
  scale_y_continuous("G2/M score")
p1 = plot.mystyle(p1)
p2 = ggplot(sc@meta.data %>% group_by(seurat_clusters,Phase) %>% summarise(num_reads=length(Phase)), aes(x=seurat_clusters, y=num_reads, fill=Phase)) + 
  geom_bar(stat="identity", position="fill") + 
  scale_x_discrete("Seurat clusters") + 
  scale_y_continuous("Fraction of cells")
p2 = plot.mystyle(p2)
p = CombinePlots(plots=list(p1, p2), legend="bottom")
p = plot.mystyle(p, title="Cell cycle phases")
p

# UMAP with phases superimposed
p3 = DimPlot(sc, group.by="Phase", pt.size=1)
plot.mystyle(p3, title="UMAP, cells coloured by cell cycle phases", legend.title="Phase")

p3 = FeaturePlot(sc, features="S.Score", pt.size=1, min.cutoff="q1", max.cutoff="q99", cols=c("lightgrey", param$col))
p3 = plot.mystyle(p3, title="UMAP, cells coloured by S phase")
p4 = FeaturePlot(sc, features="G2M.Score", pt.size=1, min.cutoff="q1", max.cutoff="q99", cols=c("lightgrey", param$col))
p4 = plot.mystyle(p4, title="UMAP, cells coloured by G2M phase")
CombinePlots(plots=list(p3, p4))

Loupe Cell Browser integration

We export the UMAP 2D visualisation, metadata such as the cell clusters, and lists of differentially expressed genes, so you can open and work with these in the Loupe Cell Browser.

# Export UMAP coordinates
loupe.umap = as.data.frame(sc@reductions$umap@cell.embeddings)
loupe.umap$Barcode = gsub(pattern="_", replacement="-", x=rownames(loupe.umap), fixed=TRUE)
if(length(grep(pattern="-$", x=loupe.umap$Barcode, perl=TRUE))==0) loupe.umap$Barcode=paste0(loupe.umap$Barcode, "-1")
loupe.umap = loupe.umap[, c("Barcode", "UMAP_1", "UMAP_2")]
colnames(loupe.umap) = c("Barcode", "UMAP-1", "UMAP-2")
write.table(loupe.umap, file=paste0(param$path.out, "/Seurat2Loupe.Umap.csv"), col.names=TRUE, row.names=FALSE, quote=FALSE, sep=",")

# Export categorical metadata
meta.to.export = c("orig.ident", "seurat_clusters", "Phase")
if(run.hto) meta.to.export = c(meta.to.export, "HTO_maxID", "HTO_classification", "HTO_classification.global", "hash.ID")
loupe.meta = as.data.frame(sc@meta.data[,meta.to.export])
loupe.meta = cbind(Barcode=gsub(pattern="_", replacement="-", rownames(loupe.meta), fixed=TRUE), loupe.meta)
if(length(grep(pattern="-$", x=loupe.meta$Barcode, perl=TRUE))==0) loupe.meta$Barcode=paste0(loupe.meta$Barcode, "-1")
write.table(x=loupe.meta, file=paste0(param$path.out, "/Seurat2Loupe.metadata.csv"), col.names=TRUE, row.names=FALSE, quote=FALSE, sep=",")

# Export gene sets
loupe.genesets = data.frame(List=paste0("DEG_up_cluster_", sc.markers.filt.up[,"cluster"]), 
                            Name=sc.markers.filt.up[,"gene"], 
                            Ensembl=symbol.to.ensembl[sc.markers.filt.up[,"gene"]])
loupe.genesets = rbind(loupe.genesets, 
                       data.frame(List=paste0("DEG_down_cluster_", sc.markers.filt.down[,"cluster"]), 
                            Name=sc.markers.filt.down[,"gene"], 
                            Ensembl=symbol.to.ensembl[sc.markers.filt.down[,"gene"]]))

genesets.to.export = list(genes.cc.s.phase=genes.s[,2], genes.cc.g2m.phase=genes.g2m[,2])
for(i in names(genesets.to.export)){
  tmp.genes = genesets.to.export[[i]]
  tmp.genes = tmp.genes[tmp.genes %in% names(symbol.to.ensembl)]
  loupe.genesets = rbind(loupe.genesets,
                         data.frame(List=i,
                                    Name=tmp.genes,
                                    Ensembl=symbol.to.ensembl[tmp.genes]))
}

write.table(loupe.genesets, file=paste0(param$path.out, "/Seurat2Loupe.genesets.csv"), col.names=TRUE, row.names=FALSE, quote=FALSE, sep=",")

Output files

All files generated with this report are written into the Seurat output folder:

• Gene identifiers
  • Seurat/GeneIds.txt: Table that contains several identifiers (columns) per gene (rows)
    
• Differentially expressed genes
  • Seurat/Markers.xlsx: Excel file with one tab per cell cluster
    
• Loupe Cell Browser files
  • Seurat/Seurat2Loupe.Umap.csv: Seurat UMAP 2D visualisation
    
  • Seurat/Seurat2Loupe.metadata.csv: Seurat cell clusters
    
  • Seurat/Seurat2Loupe.genesets.csv: Seurat differentially expressed genes
    
• Functional enrichment of differentially expressed genes per cell cluster
  • Seurat/Functions_DEG_down_cluster_1.xlsx, Functions_DEG_up_cluster_1.xlsx, …: Excel files with one tab per database

save.image(paste0(param$path.out, "/Seurat.scAnalysis.RData"))